scholarly journals Comparative analysis of routed flood frequency for reservoirs in parallel incorporating bivariate flood frequency and reservoir operation

2021 ◽  
Author(s):  
Ting Zhou ◽  
Juliang Jin
Keyword(s):  
Comparative Analysis ◽  
Reservoir Operation ◽  
Flood Frequency

scholarly journals Using comparative analysis to teach about the nature of nonstationarity in future flood predictions

2012 ◽  
Vol 16 (5) ◽  
pp. 1269-1279 ◽  
Author(s):  
S. B. Shaw ◽  
M. T. Walter
Keyword(s):  
Comparative Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Atmospheric Rivers ◽  
Changing Climate ◽  
Annual Peak ◽  
The Us ◽  
Hydrologic System ◽  
Daily Flows

Abstract. Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students' insights into the degree and predictability of future non-stationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics.

scholarly journals Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 475 ◽  
Author(s):  
Ting Zhou ◽  
Zhiyong Liu ◽  
Juliang Jin ◽  
Hongxiang Hu
Keyword(s):  
Risk Assessment ◽  
Frequency Analysis ◽  
Flood Risk ◽  
Reservoir Operation ◽  
Integrated Approach ◽  
Flood Frequency ◽  
Central China ◽  
Flood Frequency Analysis ◽  
Flood Peak ◽  
Flood Risks

Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs using different frequency definitions (copula “Or” and “And” scenario), and how these definitions affect flood risks. The approach was applied to the Meishan reservoir in central China. A set of flood hydrographs with 0.01 frequency under copula “Or” and “And” definitions were constructed, respectively. Upstream and downstream flood risks incorporating reservoir operation were calculated for each scenario. Comparisons between flood risks from univariate and bivariate flood frequency analysis showed that bivariate flood frequency analysis produced less diversity in the results, and thus the results are more reliable in risk assessment. More importantly, the peak-volume combinations in a bivariate approach can be adjusted according to certain prediction accuracy, providing a flexible estimation of real-time flood risk under different prediction accuracies and safety requirements.

scholarly journals Using comparative analysis to teach about the nature of nonstationarity in future flood predictions

2011 ◽  
Vol 8 (6) ◽  
pp. 11387-11411
Author(s):  
S. B. Shaw ◽  
M. T. Walter
Keyword(s):  
Comparative Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Atmospheric Rivers ◽  
Changing Climate ◽  
Annual Peak ◽  
The Us ◽  
Hydrologic System ◽  
Daily Flows

Abstract. Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students' insights into the degree and predictability of future non-stationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analyses could be incorporated into a number of other traditional hydrologic topics.

scholarly journals Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects

2019 ◽  
Vol 23 (11) ◽  
pp. 4453-4470 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jun Xia ◽  
Chong-Yu Xu ◽  
Cong Jiang ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Frequency Analysis ◽  
Reservoir Operation ◽  
Storage Capacity ◽  
Flood Frequency ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Exceedance Probability ◽  
Antecedent Rainfall ◽  
Reservoir Storage

Abstract. Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

Comparative Analysis of Genetic Algorithms and Particle Swarm Optimization Algorithms for Optimal Reservoir Operation

2011 ◽  
Vol 90-93 ◽  
pp. 2727-2733
Author(s):  
Ruan Yun
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Population Size ◽  
Reservoir Operation ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Global Optimal

Apart from traditional optimization techniques, modern heuristic optimization techniques, like genetic algorithms (GA), particle swarm optimization algorithm (PSO) have been widely used to solve optimization problems. This paper deals with comparative analysis of GA and PSO and their applications in a reservoir operation problem. Extensive component analysis, parameter sensitivity analysis of GA and PSO show that both GA and PSO can be used for optimal reservoir operation, but they display different features. GA can obtain very high approximate global optimal solutions of the problem with a high stability and a high computing efficiency, but it can’t obtain the problem’s accurate global optimal solutions. For GA, population size and mutation rate are two main parameters affect its solution qualities. Comparative to GA, PSO can obtain the accurate global optimal solutions of the problem with a higher computing efficiency, but with a less stability. For PSO, population size and velocity parameter are two main parameters affect its solution qualities.

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